Prediction system and prediction method

ABSTRACT

Provided is a prediction system for calculating a prediction value related to a prediction target to which prediction in an arbitrary period is adapted. The prediction system includes a storage device which records a plurality of data used to calculate the prediction value and a control device which includes a predetermined operation model and applies the plurality of data to the operation model to calculate the prediction value. The control device changes the operation model, on the basis of information of respective temporal attributes of the plurality of data.

TECHNICAL FIELD

The present invention relates to a system and method for predicting apredetermined target such as future supply and demand of power, forexample, a prediction system and method for predicting a future powerdemand to be used for managing the power supply and demand.

BACKGROUND ART

Conventionally, this type of system is implemented in a power businessfield. For example, an electric utility should supply electricity tousers, on the basis of an electricity supply contract. The electricutility can create a necessary amount of electricity by power generationautonomously. However, when an amount of electricity is likely to beinsufficient, the electric utility previously procures the electricityfrom other electric utilities and supplies the electricity to the users.

When a procurement amount of power exceeds a sales amount ofelectricity, this becomes a burden to the electric utility. For thisreason, the electric utility adjusts the procurement amount ofelectricity so that the procurement amount and the sales amount ofelectricity are matched as much as possible at each settling time.Therefore, it is important to accurately predict a total power demand ofall the users.

PTL 1 discloses a demand prediction model for selecting a power demandpattern according to an environmental condition of a day on which apower demand is to be predicted, acquiring a maximum value and a minimumvalue of a power demand amount at an expected temperature of theprediction day from data of a power demand amount by temperature, andcalculating a power demand amount at each unit time of the predictionday using them.

CITATION LIST Patent Literature

PTL 1: JP 2014-180187 A

SUMMARY OF INVENTION Technical Problem

Even if the power demand amount is predicted, an error occurs withrespect to an actual power demand amount. For this reason, in PTL 1, thepower demand pattern according to the environmental condition of the dayon which the power demand amount is to be predicted is selected toreduce the error.

However, the invention of PTL 1 is insufficient to eliminate the errorof the prediction value of the power demand amount.

Accordingly, an object of the present invention is to provide aprediction system and method capable of further reducing an error of aprediction value than the related art.

Solution to Problem

In order to solve the above problem, in the present invention, there isprovided a prediction system for calculating a prediction value relatedto a prediction target to which prediction in an arbitrary period isadapted. The prediction system includes a storage device which records aplurality of data used to calculate the prediction value and a controldevice which includes a predetermined operation model and applies theplurality of data to the operation model to calculate the predictionvalue. The control device changes the operation model, on the basis ofinformation of respective temporal attributes of the plurality of data.

Further, in the present invention, there is provided a prediction methodfor causing a control device to calculate a prediction value related toa prediction target to which prediction in an arbitrary period isadapted. The control device reads a plurality of data used to calculatethe prediction value from a storage device, applies a predeterminedoperation model to the plurality of data to calculate the predictionvalue, and changes the operation model, on the basis of information ofrespective temporal attributes of the plurality of data.

Advantageous Effects of Invention

According to the present invention, it is possible to realize aprediction system and method capable of further reducing an error of aprediction value than the related art.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a device configuration diagram showing a configuration of asupply and demand management system according to the present embodiment.

FIG. 2 is a block diagram showing a configuration of a prediction systemaccording to the present embodiment.

FIG. 3 is a flowchart showing a processing procedure of predictionprocessing.

FIG. 4 is a block diagram showing a configuration of a prediction systemaccording to a first embodiment of a representative curve calculationmodule.

FIG. 5 is a block diagram showing a configuration of a prediction systemaccording to a second embodiment of a representative curve calculationmodule.

FIG. 6 is a block diagram showing a configuration of a prediction systemaccording to a first embodiment of a correction value calculationmodule.

FIG. 7 is a block diagram showing a configuration of a prediction systemaccording to a second embodiment of a correction value calculationmodule.

FIG. 8 is a block diagram showing a configuration of a prediction systemaccording to a first embodiment of a representative curve correctionmodule.

FIG. 9 is a block diagram showing a configuration of a prediction systemaccording to a second embodiment of a representative curve correctionmodule.

FIG. 10 is a block diagram showing a configuration of a predictionsystem according to a second embodiment of a representative curvecorrection module.

FIG. 11 is a conceptual diagram showing an effect of the presentembodiment.

FIG. 12 is a conceptual diagram showing an effect of the presentembodiment.

DESCRIPTION OF EMBODIMENTS

An embodiment of the present invention will be described in detail belowwith reference to the drawings.

(1) Configuration of Supply and Demand Management System According toPresent Embodiment

FIG. 1 shows a hardware block diagram of a supply and demand managementsystem. A supply and demand management system 1 accurately predicts avalue such as a demand amount of power in a future predetermined period,on the basis of a result amount of a power demand in the past, therebyenabling supply and demand management of power such as formulation oradjustment of an operation plan of a generator and formation oradjustment of a procurement transaction plan of power from otherelectric utilities.

The supply and demand management system 1 includes terminal devices suchas a computers mainly, which are owned by a system operator systemmodule 7, a transaction market operator system module 8, a publicinformation provider system module 9, and a customer system module 10,respectively, and a network (111 and 112) such as a LAN that connectsthese devices to each other so as to enable mutual communication. Anelectric utility system module 2 includes a supply and demand managersystem module 3, a sales manager system module 4, a transaction managersystem module 5, and a facility manager system module 6.

The supply and demand manager system module 3 is a system used by adepartment or a person responsible for predicting a demand amount in afuture predetermined period in an operation time unit of a unit of 30minutes, for example, on the basis of a sales plan possessed by thesupply and demand manager or a future sales plan, and managing aprocurement amount of power so that the predicted demand amount can besatisfied, and includes a prediction operation device 30 that calculatesa prediction value of a demand and an information input/output terminal31 that exchanges data with the device.

The sales manager system module 4 is used by a department or a personresponsible for performing formulation of a sales plan of electricity ina long period or a short period or new contract conclusion ofelectricity supply and management of an existing electricity supplycontract with respect to a customer, and includes a sales managementdevice 40 that manages information of the formulated sales plan and thecustomer who has concluded the electricity supply contract.

The transaction manager system module 5 is a system used by a departmentor a person responsible for planning and executing a transaction toprocure electricity through a direct contract with other electricutility or an exchange, and includes a transaction management device 50that manages information of an electricity procurement transaction planand a concluded electricity procurement contract and exchanges telegramsconcerning transactions with other electric utility and the exchange.

The facility manager system module 6 is a system used by a department ora person responsible for formulating and executing an operation plan ofa power generation facility owned by an own company or a powergeneration facility capable of being included in an electricityprocurement plan of the own company and not owned by the own company,and includes a facility management device 60 and a control device 61that receives a control signal from the facility management device 60and actually executes control of the power generation facility. Thefacility management device 60 transmits control signals to manageinformation of the power generation facility, formulate the operationplan of the power generation facility, and execute the operation plan.

On the other hand, the system operator system module 7 is a system usedby a business operator who manages transmission and distribution systemfacilities extending over a wide area and stores a measurement valueobtained by measuring a demand result of each customer of the area, andincludes a system information management device 70 that distributes themeasured result value of the customer.

The transaction market operator system module 8 is a system used by abusiness operator who integrally manages information and proceduresnecessary for performing power transactions with respect to a pluralityof electric utilities, and includes a market operation management device80 that distributes information on power transactions and performscollation processing of orders received from the respective electricutilities.

The public information provider system module 9 is a system used by abusiness operator who provides past observation information on a weathersuch as a temperature, a humidity, and a solar radiation amount andfuture forecast information, and includes a public informationdistribution device 90 that distributes the observation information andthe forecast information of the weather.

The customer system module 10 is a system used by an individual or acorporation having a load facility or a power generation facility, andincludes an information input/output terminal 101 that transmitsinformation capable of affecting a demand or power generation tendencysuch as owned facilities, owned installations, industry types,occupants, and locations to the electric utility system module 2 or thesystem operator system module 7 and a measurement device 100 thatmeasures result amounts of the demand and the power generation.

(2) Prediction Function According to Present Embodiment

Next, a prediction function with which the supply and demand managementsystem 1 according to the present embodiment is equipped will bedescribed. The supply and demand management system 1 is equipped with aprediction function of predicting a power demand amount.

FIG. 2 shows a prediction system 12 according to the present embodimentthat constitutes a part of the supply and demand management system 1.The prediction system 12 according to the present embodiment is a systemfor predicting a power demand is equipped with the prediction function,and includes a prediction operation device 30 and a sales managementdevice 40.

The prediction operation device 30 calculates data (hereinafter,referred to as a curve showing a temporal transition) showing acharacteristic of a change such as a temporal increase or decrease of ademand value in a preset arbitrary future period, on the basis ofattribute result information 311, attribute forecast information 312,and demand result information 406 held by the sales management device40, corrects the calculated curve to calculate a prediction value, andholds the prediction value in prediction result information 313.

Here, the prediction result information 313 includes at leastinformation of the calculated curve showing the temporal transition ofthe demand value in the future period and the prediction valuecalculated by correcting the curve. In addition, the predictionoperation device 30 transmits the prediction result information 313calculated as described above to the facility management device 60 andthe transaction management device 50.

The sales management device 40 holds the demand result information 406and sales contract budgetary information 407. Of these, the demandresult information 406 is information including past demand resultinformation 406 of customers who have concluded a contract and customerswho are likely to conclude a contract, which is acquired from themeasurement device 100 and the system information management device 70.For example, the demand result information 406 includes a result valueof a power demand of each customer system module 10 for every 30 minutesfor past several years.

Further, the sales contract budgetary information 407 is informationcreated by the sales management device 40 or the sales manager systemmodule 4. For example, the sales contract budgetary information 407includes information of a supply start date and time, a supply end dateand time, and a contract power capacity of each customer system module10 for which a contract is already concluded or each customer systemmodule 10 for which a contract is scheduled to be concluded, in units ofdays, weeks, months, or years over past and future arbitrary periods.

The prediction operation device 30 is constituted by an informationprocessing device such as a personal computer, a server computer, and ahandheld computer, and includes a central processing unit (CPU) 301 tobe a control device to integrally control an operation of the predictionoperation device 30, an input device 302, an output device 303, acommunication device 304, and a storage device 305.

The input device 302 is constituted by a keyboard, a mouse, or acombination thereof and the output device 303 is constituted by adisplay, a printer, or a combination thereof. Further, the communicationdevice 304 is configured to include a network interface card (NIC) forconnection with a wireless LAN or a wired LAN. Further, the storagedevice 305 is constituted by storage media of a random access memory(RAM) and a read only memory (ROM).

Various computer programs of a timeliness index value setting module306, a reliability index value setting module 307, a representativecurve calculation module 308, a correction value calculation module 309,and a representative curve correction module 310 are stored in thestorage device 305.

The timeliness index value setting module 306 is a program that has afunction of evaluating timeliness on whether each sample value(hereinafter, referred to as sample data) to be data used for predictingthe attribute result information 311 and the demand result information406 has a temporal correlation with a future period to be a presetprediction target and calculating an index value showing the timelinesscorresponding to each sample data.

The reliability index value setting module 307 is a program that has afunction of evaluating reliability such as a variation range of anestimation result for various estimation data calculated in the middleof a processing process in the prediction operation device 30 andcalculating an index value showing the reliability corresponding to eachof the various estimation data.

The representative curve calculation module 308 is a program that has afunction of calculating a curve showing a temporal transition of aprediction target in a future period to be a preset prediction target,using the attribute result information 311, the demand resultinformation 406, the sales contract budgetary information 407, or acombination thereof.

The correction value calculation module 309 is a program that has afunction of calculating a correction value (hereinafter, referred to ascorrection data) to change the amplitude or the frequency of the curvecalculated by the representative curve calculation module 308, on thebasis of the sample data used for predicting the attribute resultinformation 311 and the demand result information 406 and the indexvalue showing the timeliness calculated by the timeliness index valuesetting module 306.

The representative curve correction module 310 is a program that has afunction of changing the amplitude or the frequency of the curvecalculated by the representative curve calculation module 308, on thebasis of the index value showing the reliability of each of thecorrection data calculated by the correction value calculation module309 and the correction data calculated by the reliability index valuesetting module 307.

Further, databases of the attribute result information 311, theattribute forecast information 312, and the prediction resultinformation 313 are stored in the storage device 305.

The attribute result information 311 is a database in which pastmeasurement data of attribute information that can explain a shape ofthe curve showing the temporal transition of the power demand to be theprediction target and an increase and decrease in the scale of thedemand is stored.

The attribute result information 311 includes information such ascalendar day information, weather information, information showing theoccurrence or nonoccurrence of unexpected events of a typhoon and anevent, or industry movement information showing actual conditions ofvarious industries capable of affecting the power demand. The calendarday information is day type information showing a year, a month, a dayof the week, a weekday, a holiday, or a combination thereof and theweather information shows a temperature, a humidity, a solar radiationamount, daylight hours, an atmospheric pressure, a wind speed, or acombination thereof.

The attribute forecast information 312 is a database in which forecastdata in a preset future period for each of the various attributeinformation stored in the attribute result information 311 is stored.

The prediction result information 313 is a database in which a finalprediction result calculated by the prediction operation device 30 orintermediate data in the calculation process is stored. The predictionresult information 313 includes information of prediction result data ofa prediction target in the preset future period, curve data representinga temporal transition of the prediction target in the same period,correction data to correct the curve, or an operation model(hereinafter, referred to as a model) to be an operation expression usedfor a calculation operation of these data.

On the other hand, the sales management device 40 is constituted by aninformation processing device such as a personal computer, a servercomputer, and a handheld computer, for example, and includes a CPU 401to integrally control an operation of the sales management device 40, aninput device 402, an output device 403, a communication device 404, anda storage device 405.

The input device 402 is constituted by a keyboard, a mouse, or acombination thereof and the output device 403 is constituted by adisplay or a printer. Further, the communication device 404 isconfigured to include an NIC for connection with a wireless LAN or awired LAN.

The storage device 405 is constituted by storage media of a RAM and aROM and databases of the demand result information 406 and the salescontract budgetary information 407 are stored in the storage device 405.

The demand result information 406 is a database in which informationreceived and acquired from the measurement device 100 and the systeminformation management device 70 is stored and various informationincluding past demand result information of the customer system module10 for which a contract is already concluded or the customer systemmodule 10 for which a contract is scheduled to be concluded is stored inthe demand result information 406. A granularity of the resultinformation is, for example, a unit of 30 minutes and a period isseveral days to several years.

The sales contract budgetary information 407 is a database in whichschedule and result information of a sales plan of electricity createdby the sales manager system module 4 is stored, and includes informationsuch as a supply start date and time, a supply end date and time, and acontract power capacity of each customer system module 10 for which acontract is already concluded or each customer system module 10 forwhich a contract is scheduled to be concluded, in units of days, weeks,months, or years over past and future arbitrary periods.

(3) Prediction Processing

FIG. 3 shows a processing procedure of prediction processing in theprediction system 12. This processing is processing that starts when theprediction operation device 30 receives an input operation from thesupply and demand manager system module 3 or at a preset time intervalor time, and processing of steps S11 to S15 is executed by theprediction operation device 30.

Actually, the processing is executed on the basis of the variouscomputer programs stored in the storage device 305 and the CPU 301 ofthe prediction operation device 30. For convenience of explanation,processing subjects are explained as the various computer programs ofthe prediction operation device 30.

First, the timeliness index value setting module 306 extracts a sampledata set to be used for prediction from the demand result information406 and the attribute result information 311. Then, for each sample datain the sample data set, timeliness to be a degree scale of a temporalcorrelation with the prediction target period is evaluated and an indexvalue showing the timeliness is calculated (S11).

Here, in the evaluation of the temporal correlation of each sample datain the sample data set and the prediction target period, for example,sample data closer in time to the prediction target period is evaluatedas a strong correlation. In addition, the index value showing thetimeliness calculated on the basis of the above evaluation is calculatedas a function of time. For example, the index value is calculated as thereciprocal of the number of days from the prediction target period or apassage interval. In this case, the larger the index value is, thestronger the correlation with the prediction target period is.

The evaluation of the temporal correlation of each sample data in thesample data set and the prediction target period may be an evaluationbased on a time periodic variation tendency of the prediction target.For example, when the prediction target has a seasonal periodicvariation tendency, previous year sample data of the same season as theprediction target period is evaluated as strong correlation and theindex value showing the timeliness is calculated by using atrigonometric function on a temporal axis.

For example, when the prediction target has a day-of-week periodicvariation tendency, sample data of the same day as the prediction targetperiod is evaluated as strong correlation and the index value showingthe timeliness is “1” in the case of the sample data of the same day asthe prediction target period and “0” in the case of sample data of theother days. This is the same even when the prediction target has aperiodic variation tendency based on a day type showing a weekday or aholiday, for example, in addition to the day of the week.

Next, the representative curve calculation module 308 calculates a curveshowing a temporal transition of a value of the prediction target, usingthe demand result information 406, the attribute result information 311,and the attribute forecast information 312 (S12).

In calculating the curve showing the temporal transition of the value ofthe prediction target, the index value showing the timeliness calculatedby the timeliness index value setting module 306 is used. Specifically,after the index value showing the timeliness is multiplied as aweighting coefficient for each sample data, the curve is calculated. Asa result, a curve further emphasizing sample data having a strongtemporal correlation with the prediction target period is calculated anda curve closer to the shape of the curve predicted to be observed in theprediction target period can be calculated.

On the other hand, the correction value calculation module 309calculates correction data to correct the curve calculated by therepresentative curve calculation module 308, using the demand resultinformation 406, the attribute result information 311, and the attributeforecast information 312 (S13).

Here, the correction of the curve specifically means a change in theamplitude to be scale correction on a quantitative axis of the curve ora change in the frequency to be scale correction on a temporal axis ofthe curve. Therefore, the correction data is a prediction value of aprediction target at an arbitrary time within the prediction targetperiod, a prediction value of a maximum value or a minimum value in anarbitrary period within the prediction target period, or a predictionvalue of an integration value.

In calculating the correction data, the index value showing thetimeliness calculated by the timeliness index value setting module 306is used. Specifically, after the index value showing the timeliness ismultiplied as a weighting coefficient for each sample data, a predictionvalue of a prediction target at an arbitrary time within the predictiontarget period, a prediction value of a maximum value or a minimum valuein an arbitrary period within the prediction target period, or aprediction value of an integration value to be the correction data iscalculated.

As a result, correction data further emphasizing sample data having astrong temporal correlation with the prediction target period can becalculated and can be corrected to a curve to calculate a predictionvalue closer to the value of the prediction target predicted to beobserved in the prediction target period.

Next, the reliability index value setting module 307 evaluates two typesof reliabilities, that is, quantitative and temporal reliabilities foreach of a prediction value for each of a prediction target at anarbitrary time within the prediction target period, a prediction valueof a maximum value or a minimum value in an arbitrary period within theprediction target period, and a prediction value of an integration valueto be the calculated correction data, and calculates index valuesshowing the reliabilities (S14). The index value showing the reliabilityis, for example, a reliable section or a prediction section of eachcorrection data.

Finally, the representative curve correction module 310 changes theamplitude or the frequency of the curve or both the amplitude and thefrequency using the correction data of the curve showing the temporaltransition of the prediction target in the prediction target period,calculated by the representative curve calculation module 308, and thecurve calculated by the correction value calculation module 309 andstores a correction result as a prediction value in the predictionresult information 313 (S15).

When the correction is performed, the index value showing thereliability for each correction data, calculated by the reliabilityindex value setting module 307, is used. Specifically, the index valueshowing the reliability is used as a weighting coefficient at the timeof correction, thereby controlling a correction amount of the curve.With the above processing, the prediction processing according to thepresent embodiment ends.

(4) Details of Each Processing Module

(4-1) First embodiment of representative curve calculation module

FIG. 4 shows a first embodiment of the representative curve calculationmodule 308 in the prediction system 12. The representative curvecalculation module 308 includes a time unit clustering processing module308A1 and a time unit profiling processing module 308A2.

The representative curve calculation module 308 according to the presentembodiment calculates a curve 308B showing a temporal transition of theprediction target in the future period to be the preset predictiontarget, using the demand result information 406, the attribute resultinformation 311, and the attribute forecast information 312 to be thedemand value data as input samples.

(4-1-1) Time Unit Clustering Processing Module

The time unit clustering module 308A1 classifies the sample dataextracted from the demand result information 406, on the basis of afeature amount showing a periodic change of the prediction target.

First, the time unit clustering module 308A1 divides the sample dataextracted from the demand result information 406 with a preset timegranularity and calculates a new second sample value (hereinafter,referred to as second sample data) set (second sample data set). Byusing frequency analysis of a Fourier transform or a wavelet transformfor each of the divided sample data, a feature amount showing a periodicfeature is calculated. Then, clustering processing is performed on thecalculated feature amount and sample data having similar waveform shapesin units of 24 hours are, for example, classified as a cluster(hereinafter, referred to as a time cluster).

Known methods may be applied to an algorithm used for the clusteringprocessing using the cluster. As the known methods, k-means to be anunsupervised clustering algorithm of neighboring optimization, an EMalgorithm, and spectral clustering are exemplified. Further, as theknown methods, unsupervised support vector machine (SVM) to be anunsupervised clustering algorithm of optimization of an identificationsurface, a VQ algorithm, and self-organizing maps (SOM) are exemplified.

In calculating the feature amount, each sample data is normalized sothat an average is 0 and a standard deviation is 1, for example. Byapplying the normalization, only a periodic feature that does not dependon the magnitude of the value of each sample data is extracted.

(4-1-2) Time Unit Profiling Processing Module

The time unit profiling processing module 308A2 performs specificationof commonly existing attributes and calculation of a range of valuesthereof for each time cluster, calculated by the time unit clusteringmodule 308A1, thereby identifying a discriminator for discriminatingeach time cluster.

Specifically, using a supervised learning algorithm that uses a sampledata set in which identifiers of a number and a name specifying eachtime cluster are used as teacher labels and each attribute informationstored in the attribute result information 311 is used as a predictor, adiscriminator in which compatibility with the sample data set becomeshighest is identified. Here, an index to measure the compatibility is anindex value showing a discrimination degree of the sample data set suchas the entropy and the Gini coefficient, a test error at the time ofcross validation performed in the process of the discriminatoridentification, or the like. In addition, the discriminator is one ofelements constituting an operation model for calculating the curveshowing the temporal transition of the prediction target.

At the time of the above calculation, the index value showing thetimeliness for each sample data, calculated by the timeliness indexvalue setting module 306, is used as a weighting coefficient. As aresult, a discriminator in which a change with lapse time (hereinafter,referred to as secular change) of a prediction target or the like hasbeen reflected can be calculated. In other words, a structure of thediscriminator to be the model for calculating the curve showing thetemporal transition of the prediction target can be changed according tothe index value showing the timeliness.

By inputting the attribute forecast information 312 to the calculateddiscriminator, a time cluster where the curve showing the temporaltransition of the prediction target in the prediction target period isexpected to belong is identified. A method of calculating the curveshowing the temporal transition of the prediction target from theidentified time cluster is a method of calculating the curve as anarithmetic mean of a sample data group belonging to the identified timecluster, for example. Alternatively, the curve is calculated by aweighted mean with the assignment probability of all time clusterscalculated from the discriminator as a weighting coefficient. Aprocessing portion calculated by the curve showing the temporaltransition of the prediction target from the identified time cluster isone of the elements constituting an operation model for calculating thecurve showing the temporal transition of the prediction target.

Known methods may be applied to a discriminator calculation algorithm.As the known methods, CART, ID3, a random forest decision tree learningalgorithm, and a support vector machine (SVM) identification planelearning algorithm are exemplified.

(4-2) Timeliness Index Value Setting Module

For each sample data in the sample data set, the timeliness index valuesetting module 306 evaluates timeliness to be a degree scale of atemporal correlation of each sample data in the sample data set and theprediction target period and calculates an index value showing thetimeliness.

Specifically, the sample data set to be used for prediction is extractedfrom the demand result information 406 and the attribute resultinformation 311. In addition, for each sample data in the sample dataset, timeliness to be a degree scale of a temporal correlation with theprediction target period is evaluated and an index value showing thetimeliness is calculated.

Here, in the evaluation of the temporal correlation with the predictiontarget period, for example, sample data closer in time to the predictiontarget period is evaluated as a strong correlation. In addition, theindex value showing the timeliness calculated on the basis of the aboveevaluation is calculated as a function of time. For example, the indexvalue may be calculated as the reciprocal of the number of days from theprediction target period or a passage interval. In this case, the largerthe index value is, the stronger the correlation with the predictiontarget period is.

The evaluation of the temporal correlation with the prediction targetperiod may be an evaluation based on a time periodic variation tendencyof the prediction target. For example, when the prediction target has aseasonal periodic variation tendency, previous year sample data of thesame season as the prediction target period is evaluated as strongcorrelation and the index value showing the timeliness is calculated byusing a trigonometric function on a temporal axis.

For example, when the prediction target has a day-of-week periodicvariation tendency, sample data of the same day as the prediction targetperiod is evaluated as strong correlation and the index value showingthe timeliness is “1” in the case of the sample data of the same day asthe prediction target period and “0” in the case of sample data of theother days. This is the same even when the prediction target has aperiodic variation tendency based on a day type showing a weekday or aholiday, for example, in addition to the day of the week.

As described above, the index value showing the timeliness calculated bythe timeliness index value setting module 306 is used as a weightingcoefficient in the discriminator calculating process in the time unitprofiling processing module 308A2. Also, the index value is used for anoperation in the correction value calculation module 309. The sameeffect is obtained in any case and it is possible to execute learningprocessing further emphasizing the sample data having a strong temporalcorrelation with the prediction target period. Therefore, data to becalculated becomes more accurate data in which the secular change of theprediction target has been reflected.

(4-3) First Embodiment of Correction Value Calculation Module

FIG. 6 shows a first embodiment of the correction value calculationmodule 309 in the prediction system 12. The correction value calculationmodule 309 includes a model identification module 309A1 and a correctionvalue estimation module 309A2.

The correction value calculation module 309 according the presentembodiment calculates correction data to correct the curve calculated bythe representative curve calculation module 308, using the demand resultinformation 406, the attribute result information 311, and the attributeforecast information 312.

Here, the correction of the curve specifically means a change in theamplitude to be scale correction on a quantitative axis of the curve ora change in the frequency to be scale correction on a temporal axis ofthe curve. Therefore, the correction data is a prediction value of aprediction target at an arbitrary time within the prediction targetperiod, a prediction value of a maximum value or a minimum value in anarbitrary period within the prediction target period, or a predictionvalue of an integration value. The prediction value is used as acorrection reference point.

(4-3-1) Model Identification Module

First, the model identification module 309A1 identifies a model used fora calculation operation of a prediction value of a prediction target atan arbitrary time within the prediction target period, a predictionvalue of a maximum value or a minimum value in an arbitrary periodwithin the prediction target period, or a prediction value of anintegration value to be the correction data, using the demand resultinformation 406 and the attribute result information 311.

For example, when a prediction value of a prediction target at anarbitrary time within the prediction target period, a prediction valueof a maximum value or a minimum value in an arbitrary period within theprediction target period, or a prediction value of an integration valueto be the correction data is set to y and an explanatory variable of yis set to x, a relation of the following formula is realized between yand x.[Formula 1]y=ax ₁ +bx ₁ ² +cx ₂ +dx ₂ ²  (1)

Here, x1 and x2 are, for example, a mean temperature and a result valueof y of a previous day, respectively, and specific numerical values arestored in the attribute result information 311. The model identificationmodule identifies an operation model by estimating coefficients a, b, c,and d described in the formula (1) so that compatibility between x and ybecomes highest. Specifically, the coefficients are estimated by a leastsquares method, for example. At that time, an index to measure thecompatibility is a residual sum of squares of the identified model andsample data and the compatibility becoming highest means that theresidual sum of squares becomes smallest.

In estimating the coefficients, the index value showing the timelinessfor each sample data, calculated by the timeliness index value settingmodule 306, is used. Specifically, the coefficients a, b, c, and d areestimated by a weighted least squares method with an index value showingthe timeliness as a weighting coefficient and are calculated as anidentification result of a model used for a calculation operation of thecorrection data. As a result, more accurate correction data in which thesecular change of y has been reflected can be calculated.

(4-3-2) Correction Value Estimation Module

In addition, the correction value estimation module 309A2 inputs aforecast value of the explanatory variable x stored in the attributeforecast information 312 to the model used for a calculation operationof the correction data, calculated by the model identification module309A1, thereby calculating a correction value of the curve showing thetemporal transition in the prediction target period as the correctiondata.

Here, the correction data includes at least two types of information tobe information (correction data 309B1) of the correction value itselfand information (correction data 309B2) on a time range of eachcorrection value. The information on the time range of each correctionvalue is, for example, in a case where a maximum value or a minimumvalue in an arbitrary period within the prediction target period is usedas the correction data, a range of time at which each value appears.

In addition, the correction value estimation module 309A2 calculatessample error data at the time of calculation by the least squares methodas a part of the correction data. The data is data used by thereliability index value setting module 307 at the time of calculating anindex value showing the reliability. Further, the data is data of acalculation result of the least squares method executed to identify amodel used for a calculation operation of the correction data,calculated by the model identification module 309A1, or a model used fora calculation operation of the correction data by the modelidentification module 309A1.

When the sales contract budgetary information 407 to be budgetaryinformation of a power sales contract can be used, further accurateprediction can be made. Specifically, the correction data 309B1calculated by the correction value estimation module 309A2 is oncedivided by a total contract power capacity at the present time, and anoriginal unit correction value per contract power capacity iscalculated.

In addition, the original unit correction value is multiplied by thetotal contract power capacity in the prediction target period andcorrection data 309B1 is newly calculated. As a result, more accuratecurve correction data can be calculated even when the number of contractcustomers increases or decreases.

(4-4) First Embodiment of Representative Curve Correction Module

FIG. 8 shows a first embodiment of the representative curve correctionmodule 310 in the prediction system 12. The representative curvecorrection module 310 includes an amplitude correction module 310A1 anda frequency correction module 310A2.

The representative curve correction module 310 according the presentembodiment changes the amplitude or the frequency of the curvecalculated by the representative curve calculation module 308, using thecorrection data calculated by the correction value calculation module309. At this time, the index value showing the reliability of eachcorrection data calculated by the reliability index value setting module307 is used, so that a more accurate prediction value is calculated.

(4-4-1) Amplitude Correction Module

First, the amplitude correction module 310A1 changes the amplitude ofthe curve 308B showing the temporal transition of the prediction targetin the prediction target period calculated by the representative curvecalculation module 308, using the correction data 309B1 calculated bythe correction value calculation module 309. Specifically, a correctedcurve f{circumflex over ( )}(t) is given by the following formula.[Formula 2]{circumflex over (f)}(t)=α+β×f(t)  (2)

Here, f(t) is a curve showing the temporal transition of the predictiontarget in the prediction target period and is a function of time t. Inaddition, α and β are change coefficients of the curve f(t). That is,the amplitude correction module 310A1 executes processing for estimatingthe change coefficients α and β so that a residual sum of squares of aprediction value of a prediction target at an arbitrary time within theprediction target period, a prediction value of a maximum value or aminimum value in an arbitrary period within the prediction targetperiod, or a prediction value of an integration value to be thecorrection data 309B1 and the corrected curve f{circumflex over ( )}(t)is minimized.

When α and β are estimated, the index value showing the reliability foreach correction data, calculated by the reliability index value settingmodule 307, is used. Specifically, after an index value showingquantitative reliability for each correction data is multiplied as aweighting coefficient for the residual of a value of each correctiondata and the curve f{circumflex over ( )}(t), the change coefficients αand β are estimated.

As a result, the curve f(t) is corrected by preferentially reducing theresidual of each correction data calculated by the reliability indexvalue setting module 307 and a value of correction data having higherreliability than correction data having lower reliability, so that finalprediction accuracy is improved.

(4-4-2) Frequency Correction Module

In addition, the frequency correction module 310A2 changes the frequencyof the curve 308B showing the temporal transition of the predictiontarget in the prediction target period calculated by the representativecurve calculation module 308, using the correction data 309B2 calculatedby the correction value calculation module 309.

For example, when the correction data is a prediction value of a maximumvalue or a minimum value in an arbitrary period within the predictiontarget period, the frequency of the curve f(t) is changed so that a meanvalue or a mode value of each appearance time range stored in thecorrection data 309B2 and corresponding time of the corrected curvef{circumflex over ( )}(t) are matched or a residual sum of squaresthereof is minimized. In addition, the corrected curve f{circumflex over( )}(t) is calculated as a prediction value of the prediction target inthe prediction target period and stored in the prediction resultinformation 313.

When the correction is performed, the index value showing thereliability for each correction data, calculated by the reliabilityindex value setting module 307, is used. Specifically, after an indexvalue showing temporal reliability for each correction data ismultiplied as a weighting coefficient for the residual of a value ofeach correction data and the curve f{circumflex over ( )}(t), correctionprocessing is executed. As a result, the curve f(t) is corrected bypreferentially reducing the residual of each correction data calculatedby the reliability index value setting module 307 and a value ofcorrection data having higher reliability than correction data havinglower reliability, so that final prediction accuracy is improved.

(4-4-3) Reliability Index Value Setting Module

The reliability index value setting module 307 evaluates two types ofreliabilities, that is, quantitative and temporal reliabilities for eachof a prediction value for each of a prediction target at an arbitrarytime within the prediction target period, a prediction value of amaximum value or a minimum value in an arbitrary period within theprediction target period, and a prediction value of an integration valueto be the calculated correction data, and calculates index valuesshowing the reliabilities.

Specifically, from a model used for a calculation operation of eachcorrection data or sample error data calculated in the modelidentification process, included in the correction data 309B1 calculatedby the correction value estimation module 309A2, a reliable section anda prediction section of the estimation value of each correction data ora variance and a standard deviation of sample errors are calculated.These are calculated as the index values showing the quantitativereliabilities.

From information of an appearance time range of a value of eachcorrection data included in the correction data 309B2 calculated by thecorrection value estimation module 309A2, a reliable section and aprediction section of each correction data on a temporal axis or avariance and a standard deviation of the information of the time rangeare calculated. These are calculated as the index values showing thetemporal reliabilities.

As described above, the representative curve correction module 310 usesthe index value showing the reliability, so that prediction accuracy ofa prediction value to be finally calculated can be improved.

The operation plan of the power generation facility operable by thefacility management device 60 is calculated on the basis of theprediction result information 313 calculated by the predictionprocessing described above and is transmitted to the control device 61.The control device 61 that has received the operation plan calculates aspecific control value of the power generation facility and executesactual control.

In addition, the transaction management device 50 creates a transactionplan related to trading of power with another electric utility or atransaction market and transmits a telegram of a trading order or ordercancellation to the market operation management device 80.

(5) Effects of Present Embodiment

As shown in FIG. 11, a calculation result of the curve showing thetemporal transition of the prediction target in the prediction targetperiod, output by the representative curve calculation module 308, isdifferent in the case of using the index value showing the timeliness ofeach sample data, calculated by the timeliness index value settingmodule 306 and the case of not using the index value.

First, a graph 501 of FIG. 11 shows a transition of a mean temperaturefor each day in a year. Here, if a most important attribute is a dailymean temperature in the discriminator of the time cluster calculated bythe time unit profiling processing module 308A2 in the representativecurve calculation module 308, a time cluster to which sample data of thesame mean temperature as the prediction target period belongs isidentified as a time cluster to which the curve showing the temporaltransition predicted to be observed in the prediction target periodbelongs.

Specifically, two time clusters shown by graphs 502 and 503 of FIG. 11are time clusters of candidates to be identified. Here, it is assumedthat the curve showing the temporal transition of the prediction targetchanges from a curve shown by the graph 502 to a curve shown by thegraph 503 after one year.

Here, it is assumed that the index value showing the timeliness of eachsample data is not used in the calculation process of the abovediscriminator. In the discriminator calculated in that case, the timeclusters of the graph 502 and graph 503 of FIG. 11 are identified asalmost the same probabilities. Therefore, the curve showing the temporaltransition of the prediction target in the prediction target period iscalculated as a mean curve of both the temporal clusters as shown by agraph 504 of FIG. 11 and a secular change of the curve cannot berecognized.

On the other hand, when the index value showing the timeliness of eachsample data is used, as shown by a graph 505 of FIG. 11, the curveshowing the temporal transition of the prediction target in theprediction target period is calculated as a shape closer to a mostrecent curve after the secular change. Therefore, final predictionaccuracy can be improved.

The index value showing the timeliness of each sample data is also usedin the correction value calculation module 309 and its effect andprinciple are the same as those described above.

In addition, FIG. 12 shows an influence on the corrected curve to be thefinal prediction result calculated by the representative curvecorrection module 310, when the index value showing the reliability forthe correction data of the curve, calculated by the reliability indexvalue setting module 307, is used.

As the index value showing the reliability for the correction data ofthe curve, calculated by the reliability index value setting module 307,there are index values showing at least two types of reliabilities ofquantitative reliability and temporal reliability.

First, a graph 601 of FIG. 12 conceptually shows the quantitativereliability. Here, as the correction data of the curve, for example, aminimum value in the early morning, a maximum value around noon, and amaximum value around the evening are used.

At this time, the index values showing the respective reliabilities aredefined as variances on a probability density function shown by graphs602, 603, and 604 of FIG. 12, respectively. To simplify the explanation,the index value is represented by high and low binary values.

Here, the reliabilities of the minimum value in the early morning andthe maximum value around the evening are high and the reliability of themaximum value around noon is low. Therefore, as shown by a graph 609 ofFIG. 12, the change in the amplitude of the curve performs correction tominimize the residual on the quantitative axis of the curve and theminimum value in the early morning and the residual on the quantitativeaxis of the curve and the maximum value around the evening, afterallowing enlargement of the residual on the quantitative axis of thecurve and the maximum value around noon.

On the other hand, a graph 605 of FIG. 12 conceptually shows thetemporal reliability. Here, similar to the above case, it is assumedthat a minimum value in the early morning, a maximum value around noon,and a maximum value around the evening are used as the correction dataof the curve.

At this time, it is assumed that the index values showing the respectivereliabilities are defined as ranges of past observation values shown bygraphs 606, 607, and 608 of FIG. 12, respectively. To simplify theexplanation, the index value is represented by high and low binaryvalues.

Here, the reliabilities of the minimum value in the early morning andthe maximum value around noon are high and the reliability of themaximum value around the evening is low. Therefore, as shown by a graph610 of FIG. 12, the change in the amplitude of the curve performscorrection to minimize the residual on the temporal axis of the curveand the minimum value in the early morning and the residual on thetemporal axis of the curve and the maximum value around noon, afterallowing enlargement of the residual on the temporal axis of the curveand the maximum value around the evening.

As described above, the curve correction in which priority is given tothe correction data with the high reliability is performed, so that thecorrected curve to be the final prediction value can be made a moreplausible curve. In other words, it is possible to obtain a predictionvalue considering usefulness such as the reliability of each of thesample values in the prediction and the processing data obtained duringthe processing.

(6) Other Embodiment of Each Module

(6-1) Second Embodiment of Representative Curve Calculation Module

In the first embodiment of the representative curve calculation module308 described above, the case where the clustering algorithm using thefeature amount showing the feature of the periodic variation of theprediction target is used as the method of calculating the curve showingthe temporal transition of the prediction target in the predictiontarget period has been described. However, the present invention is notlimited thereto. For example, the representative curve calculationmodule 308 may calculate a curve with an arithmetic mean of sample dataof the past several days of the same day type as the prediction targetdate.

Further, in the first embodiment of the representative curve calculationmodule 308 described above, the case where the data to be the predictiontarget stored in the demand result information 406 is one data measuredby one meter or one data obtained by combining a plurality of datameasured by a plurality of meters has been described. However, thepresent invention is not limited thereto. For example, in the case ofpower demand data, more accurate prediction may be realized by using thedata of the prediction target as measurement data of each meterinstalled for each customer of power.

Specifically, as shown in FIG. 5, the representative curve calculationmodule 308 further includes a measurement unit clustering processingmodule 308A3. The measurement unit clustering processing module 308A3extracts data for each meter stored in the demand result information 406as the same period of past 365 days, for example, and sets meter datafor each customer as input sample data.

By performing frequency analysis of a Fourier transform or a wavelettransform on each sample data, a feature amount showing a periodicfeature is calculated. In addition, clustering processing is performedon the calculated feature amount and sample data having similar waveformshapes in units of 365 days (8760 hours) are classified as a cluster(hereinafter, referred to as a meter cluster).

Hereinafter, the same time clustering processing and time unit profilingprocessing as those in the first embodiment of the representative curvecalculation module 308 are performed on a representative waveform ofeach meter cluster. A method of calculating the representative waveformof each meter cluster is, for example, an arithmetic mean of each metercluster.

As described above, the data is classified in advance for each of themeasurement points having similar variations of the value of theprediction target in the long period, so that the sample variance in thesample data can be reduced, and accuracy of the curve showing thetemporal transition of the prediction target in the prediction targetperiod to be calculated by the following processing can be improved.

(6-2) Second Embodiment of Correction Value Calculation Module

In the first embodiment of the correction value calculation module 309described above, the case where the amplitude or the frequency of thecurve showing the temporal transition of the prediction target in theprediction target period is corrected so that the curve is matched witha prediction value of the prediction target at an arbitrary time withinthe prediction target period, a prediction value of a maximum value or aminimum value in an arbitrary period within the prediction targetperiod, or a prediction value of an integration value or a residual sumthereof is minimized has been described. However, the present inventionis not limited thereto. For example, the change coefficients α and βshown by the formula (2) may be directly handled as correction data, forexample, the change coefficients are handled as the correction data.

The correction value calculation module 309 according to the presentembodiment further includes a correction coefficient calculation module309A3 as shown in FIG. 7 and directly predicts a correction coefficientof the curve. The correction coefficient calculation module 309A3calculates the sample data of the change coefficients α and β, using thesample data extracted from the demand result information 406.

Next, similar to the processing described using FIG. 6, the model to beused for a calculation operation of the change coefficients α and β isidentified by the model identification module 309A1 and the explanatoryvariable value extracted from the attribute forecast information 312 isinput to the identified model by the correction value estimation module309A2, so that the change coefficients α and β of the curve showing thetemporal transition of the prediction target in the prediction targetperiod are calculated.

Further, in identification of the model to be used for a calculationoperation of the change coefficients α and β in the model identificationmodule 309A1, as described using FIG. 6, the index value showing thetimeliness calculated by the timeliness index value setting module 306is used.

Further, in the first embodiment of the correction value calculationmodule 309 described above, the case where the model shown in theformula (1) is used as the model to be used for a calculation operationof the correction data has been described. However, the presentinvention is not limited thereto. For example, a form of a calculationformula and an explanatory variable to be used are arbitrary.

The explanatory variable may be a square value of the mean temperatureand may be used as a design matrix including the variable. As a result,while the model is regarded as a linear expression, a nonlinear relationbetween the prediction target and the attributes can be expressed as themodel. Specifically, similar to calculating a second sample value from asample value, a third sample value is calculated from the second samplevalue.

Further, in the first embodiment of the correction value calculationmodule 309 described above, the case where the attributes to be used asthe explanatory variables are uniquely set in advance has beendescribed. However, the present invention is not limited thereto. Forexample, a method in which explanatory variables are automaticallyselected, such as ridge regression, lasso regression, or elastic net,may be adopted. A method of calculating a new component from explanatoryvariables of principal component regression or a partial least squaresmethod may be adopted or a method using a nonlinear model of a neuralnetwork may be adopted.

In adopting any method, the index value showing the timelinesscalculated by the timeliness index value setting module 306 may be usedat the time of model identification. With the above, the same effect asthat described using FIG. 11 can be obtained and explanatory variablesaccording to the timeliness can be automatically selected. In otherwords, with the above, a change in the model used for a calculationoperation of the correction data can be performed.

In the first embodiment of the correction value calculation module 309described above, the case where which of a prediction value of aprediction target at an arbitrary time within the prediction targetperiod, a prediction value of a maximum value or a minimum value in anarbitrary period within the prediction target period, and a predictionvalue of an integration value to be the correction data is used ispreset has been described. However, the present invention is not limitedthereto. For example, the prediction value to be used may be setautomatically on the basis of the index value showing the reliabilitycalculated by the reliability index value setting module 307.

Specifically, the index values of the reliabilities for the respectivecorrection data may be compared and the correction data up to the presetranking in descending order of reliabilities may be used by thefollowing representative curve correction module 310. Further, afterstandardizing the index value showing the reliability of each correctiondata so that the index value showing the highest reliability becomes “1”and the index value showing the lowest reliability becomes “0”, all ofthe correction data may be used by the following representative curvecorrection module 310.

In this case, the correction data whose index value is “0” is not usedfor curve correction as a result, so that automatic selection of thecorrection data is achieved. Further, correction may be performed toextremely increase a difference of the magnitude of the index valueshowing the reliability with a power of the index value of eachcorrection data as a new index value. In this case, since the smallestindex value is relatively equal to “0” for the largest index value,similar to the above case where the index value is “0”, the smallestindex value is not used practically for the curve correction as aresult, so that automatic selection of the correction data is achieved.

(6-3) Second Embodiment of Representative Curve Correction Module

In the first embodiment of the representative curve correction module310 described above, the case where only the change of the amplitude,the frequency, or both the amplitude and the frequency of the curveshowing the temporal transition of the prediction target is set as thecorrection processing performed by the representative curve correctionmodule 310 has been described. However, the present invention is notlimited thereto. For example, when a seasonally peculiar or day-of-weekpeculiar error occurs steadily in a final prediction value calculated bychanging the amplitude, the frequency, or both the amplitude and thefrequency, this is a potential deviation remaining in a predictionsystem, so that processing for correcting the steady deviation may beadded.

In FIG. 8, the corrected curve calculated by the amplitude correctionmodule 310A1 and the frequency correction module 310A2 is output as thefinal prediction value. However, in the present embodiment, as shown inFIG. 9, a steady deviation correction module 310A3 corrects the steadydeviation using the corrected curve and the demand result information406 to be observed afterwards and stores it as the final predictionvalue in the prediction result information 313. As a result, therepresentative curve correction module 310 previously corrects the errorthat occurs steadily.

Specifically, as shown in FIG. 10, the representative curve correctionmodule 310 further includes the steady deviation correction module310A3. The steady deviation correction module 310A3 calculates adeviation to be a difference between a corrected curve 310A2B and thedemand result information 406 to be observed afterwards. In addition, asteady deviation quantity estimation module 310A31 identifies a modelthat explains the residual and calculates an estimation value of theresidual that can occur in the prediction target period, by theidentified model. The calculated estimation value of the residual isadded to the corrected curve 310A2B, so that the final prediction valueis calculated.

Here, an algorithm used in the steady deviation quantity estimationmodule 310A31 may be, for example, the algorithm of the representativecurve calculation module 308 described using FIG. 4. That is, the demandresult information 406 to be input is replaced with the deviation to bethe difference between the corrected curve 310A2B and the demand resultinformation 406 to be observed afterwards.

By using the above algorithm, a curve of the residual that can occur inthe prediction target period is calculated by the same processing as thetime unit clustering processing module 308A1 and the time unit profilingprocessing module 308A2.

At this time, normalization processing for the residual to be input isomitted, so that a curve of the residual to be output becomes theestimation residual including information of the quantity.Alternatively, a curve of the residual that can occur in the predictiontarget period may be calculated using a time-series analysis methodrepresented by an AR model or an ARIMA model. As described above, thesteady deviation correction module 310A3 can correct a minute variationof the prediction target that cannot be completely explained in theprediction system.

Further, at the time of model identification in the steady deviationquantity estimation module 310A31, the index value showing thetimeliness calculated by the timeliness index value setting module 306may be used. However, in this case, an input to the timeliness indexvalue setting module 306 is the above residual.

That is, by applying the index value showing the timeliness of eachresidual that has occurred in the past, a more plausible value can becalculated as the steady deviation that can occur in the predictiontarget period. In other words, the model to be used for the operation ofthe correction is appropriately changed according to the timeliness, sothat final prediction accuracy can be improved.

(7) Other Embodiment

In the embodiments described above, the case where the index valuesshowing the timeliness and the reliability are calculated by thetimeliness index value setting module 306 and the reliability indexvalue setting module 307, respectively, has been described. However, thepresent invention is not limited thereto. For example, for each of theindex values of the timeliness and the reliability, a preset value maybe directly used.

Further, in the embodiments described above, the case where a display isomitted to simplify the explanation has been described. However, thepresent invention is not limited thereto. For example, the calculationresult of each processing module or the intermediate result of eachprocessing module may be appropriately displayed through an outputdevice such as a display or a printer.

Further, in the embodiments described above, the case where the demandof the power is predicted has been described. However, the presentinvention is not limited thereto and may be applied to the case wherethere is time-series data observed with a temporal transition. Thetime-series data observed with the temporal transition is, for example,a power generation amount of solar power generation or wind powergeneration, a contract price of a power product traded at a powerexchange, a sales volume, or the like.

The present invention is not limited to a field of power and can bewidely applied to fields where there is time-series data observed withthe temporal transition, such as a communication amount of a basestation in a communication business and a local traffic amount ofvehicles or persons.

REFERENCE SIGNS LIST

-   1 supply and demand management system-   2 electric utility system module-   3 supply and demand manager system module-   4 sales manager system module-   5 transaction manager system module-   6 facility manager system module-   7 system operator system module-   8 transaction market operator system module-   9 public information provider system module-   10 customer system module-   30 prediction operation device-   31 information input/output terminal-   40 sales management device-   50 transaction management device-   60 facility management device-   61 control device-   70 system information management device-   80 market operation management device-   90 public information distribution device-   111 network-   112 network

The invention claimed is:
 1. A system for calculating a prediction valuerelated to a prediction target of future supply and demand of power towhich prediction in an arbitrary period is adapted, the systemcomprising: a storage device which records a plurality of data used tocalculate the prediction value; and a control device includes apredetermined operation model, applies the plurality of data to theoperation model to calculate the prediction value, and changes thepredetermined operation model using data determined based on respectivetemporal attributes of the plurality of data, wherein the control deviceincludes: a representative curve calculation module which calculates acurve showing a temporal transition of the prediction value of theprediction target in the arbitrary period; a correction valuecalculation module which calculates the prediction value on the basis ofcorrection of the curve calculated by the representative curvecalculation module; and a model identification module which changes thepredetermined operation model to at least one of: a curve correctionoperation model for correcting the curve, a curve calculation operationmodel for calculating the curve, and a correction value calculationoperation model for calculating a correction value of the curve, on thebasis of: the respective temporal attributes of the plurality of data,or the respective variation ranges of the plurality of data; and afacility management device that: formulates an operation plan of a powergeneration facility based on the prediction value calculated by theoperation model of the control device, and transmits a control signaland the formulated operation plan to the control device, wherein thecontrol device subsequently calculates a control value of the powergeneration facility and executes control of the power generationfacility; and executes the formulated operation plan.
 2. The predictionsystem according to claim 1, wherein the correction value calculationmodule changes the curve showing the temporal transition of theprediction value of the prediction target, on the basis of at least oneof an amplitude of the curve and a frequency of the curve.
 3. Theprediction system according to claim 1, wherein the model identificationmodule constitutes the respective variation ranges of the plurality ofdata to include at least one of variation width of a quantitativevariation width and a temporal variation width of a correction value tocorrect the curve showing a temporal transition of data showing acharacteristic of the prediction target.
 4. The prediction systemaccording to claim 1, wherein the model identification module changesthe curve correction operation model to be suitable for the correctionvalue having a smallest variation range, using a variation range of eachcorrection value.
 5. The prediction system according to claim 1, whereinthe control device uses the data determined on the basis of the temporalattributes for calculation of the curve or each correction value so asto show a temporal correlation with a prediction target period.
 6. Theprediction system according to claim 1, wherein the representative curvecalculation module calculates new second data by performing at least oneof selection of the data to calculate the curve and weighting of aninfluence degree of the data for calculation of the curve, on the basisof the temporal attributes, and the model identification module changesthe curve calculation operation model so that compatibility for thecalculated second data becomes highest.
 7. The prediction systemaccording to claim 1, wherein the representative curve calculationmodule calculates new second data by performing at least one ofselection of the data to calculate the curve and weighting of aninfluence degree of the data for calculation of the curve, on the basisof the temporal attributes, and the model identification module changesthe correction value calculation operation model so that compatibilityfor the calculated second data becomes highest.
 8. The prediction systemaccording to claim 1, wherein the representative curve calculationmodule calculates a variable value by inputting arbitrary partialvariables or all variables among variables used in the correction valuecalculation operation model to an arbitrary linear or nonlinearfunction, calculates second data based on the variable value, andcalculates new third data by performing at least one of selection of thesecond data to calculate the curve and weighting of an influence degreeof the data for calculation of the curve, on the basis of the respectivetemporal attributes of the second data, and the model identificationmodule changes the curve calculation operation model so thatcompatibility for the calculated third data becomes highest.
 9. Theprediction system according to claim 1, wherein the model identificationmodule changes the correction value calculation operation model byperforming at least one of selection and weighting of the correctionvalue calculation operation model in which the respective variationranges of the plurality of data are small, on the basis of therespective variation ranges of the plurality of data.
 10. The predictionsystem according to claim 1, wherein the correction value includes atleast one of, at least one of a quantity and a time for at least one ofa maximum value and a minimum value in each of arbitrary periods in aprediction target period and the respective variation ranges of theplurality of data.
 11. The prediction system according to claim 1,wherein the correction value includes at least one of the predictionvalue of the prediction target at each of arbitrary times in aprediction target period and the respective variation ranges of theplurality of data.
 12. The prediction system according to claim 1,wherein the correction value includes at least one of a coefficientvalue to correct the curve used in the correction value calculationmodule and the variation ranges.
 13. A method for causing a controldevice to calculate a prediction value related to a prediction target offuture supply and demand of power to which prediction in an arbitraryperiod is adapted, the method comprising: reading, using the controldevice, a plurality of data used to calculate the prediction value froma storage device; applying, using the control device, a predeterminedoperation model to the plurality of data to calculate the predictionvalue; changing, using the control device, the predetermined operationmodel based on respective temporal attributes of the plurality of data;changing, using the control device, the plurality of data to be appliedto the new operation model based on respective variation ranges of theplurality of data; calculating, using the control device, a curveshowing a temporal transition of the prediction value of the predictiontarget in the arbitrary period, calculating, using the control device,the prediction value on the basis of correction of the calculated curve,wherein the predetermined operation model is changed to at least one of:a curve correction operation model for correcting the curve, a curvecalculation operation model for calculating the curve, or a correctionvalue calculation operation model for calculating a correction value ofthe curve, on the basis of: the respective temporal attributes of theplurality of data, or the respective variation ranges of the pluralityof data, formulating, using a facility management device, an operationplan of a power generation facility based on the prediction valuecalculated by the operation model of the control device, transmitting,using the facility management device, a control signal and theformulated operation plan to the control device, calculating, using thecontrol device, a control value of the power generation facility basedon the formulated operation plan; executing, using the control device,control of the power generation facility; and executing, using thefacility management device, the formulated operation plan.